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2024 Insights Report

How AI is shaping product strategy

AI has seen a pivotal shift in the last 18 months—from proof of concept to core pillar of product strategy. Early adopters no longer have the upper hand, with the vast majority of businesses (84% in our survey) having already incorporated AI into their products.

We surveyed more than 200 leaders across the tech industry—61% of which were founders—to understand how they’re reshaping their roadmaps to stay competitive in the age of AI.

Learn the strategic moves these teams are making—like the shift to multimodal—and their answers to some of today’s biggest industry questions—like build or buy. Get the data on what leaders are doing to stay ahead of the AI curve.

Chapter 01

From adoption to innovation

Learn why leaders are reshaping their product roadmaps.

In the race to stay competitive, AI integration has become a strategic imperative. With time and money driving the push, businesses are moving fast to develop product strategies that capitalize on the transformative power of AI—before their competitors do.

out of

have implemented AI in the past 2 years

97%

of respondents said AI was not only here to stay, but that it’s a long-term necessity

“AI is going to be as essential as the Internet. A foundational layer of all tech moving forward.”

–Respondent, Head of Product

AI tides are moving fast

It’s sink or swim for a winning strategy.

How concerned are you that your competition’s AI strategy will outpace your own?

4%Extremely concerned

13%Very concerned

46%Somewhat concerned

26%Not very concerned

11%Not at all concerned

89%

That’s 89% of folks who have a little to a whole lot of concern that they will be left behind.

… and it turns out, they’re not wrong to worry.

92%

have integrated AI into their customer-facing products within the last 2 years

74%

are using AI for both internal use and customer-facing products

“Our features became outdated because AI replaced some of them. So, although we have a lot of functionality, it’s not useful—people want more.”

–Respondent, Head of Product

Why the race to put AI in products?

It comes down to time and money.

How has AI helped your business or customers?

78%

reported time savings for business and customers

60%

reported cost savings for business and customers

68%

reported improved customer productivity

In my biz, AI replaces much of the human interaction with end users. It frees up resources and increases our profitability. There is no way we’re going back to the pre-AI stone age.

–Respondent, Founder

Speech-to-text, summarization, conversational analytics, etc. are basic features expected in all products. Vendors not providing these will be ignored.

–Respondent, Product Manager

AI allows us to accomplish in 8 hours what used to take several weeks. There simply is no argument with that kind of efficiency and speed—even with a little wobble in generative variability.

–Respondent, Founder

Building with AI doesn’t just save companies time and money—it builds lasting customer loyalty by delivering tangible, transformative, and profitable results.

Two examples. One qualitative data-analysis platform strategically integrated multimodal AI models to help their customers decrease time spent analyzing data by 60%—with significantly better accuracy too. Another hiring intelligence platform integrated Speech AI for its customers and effectively slashed time spent on manual tasks by 90%.

Both of these companies watched their market value skyrocket.

Outcomes like these were impossible prior to AI—solidifying its groundbreaking capabilities as a fundamental need for end users, not just a nice to have.

Chapter 02

Innovation station

Explore how companies are making AI their own.

AI is not a buzzword. For companies looking to get and stay ahead, it’s a requirement. Innovative teams are thoughtfully refining their product strategies to leverage a holistic AI approach that meets the full spectrum of customer needs.

AI lets us focus on the tasks that really matter instead of just busy work.

–Respondent, Technical Product Manager

Anyone who's not leveraging AI will start to fall behind. It's about harnessing it correctly though.

–Respondent, Head of Product

We have AI working to solve issues on several fronts—creating a revenue model that is profitable for us.

–Respondent, Founder

Top use cases for AI integration

What’s everyone doing with it, anyway?

How are you using AI to support your products or processes?

68%

59%

49%

Customer experiences

Product recommendations
Predictive text
Virtual assistants
Automated scheduling
Smart home devices
Personalized news feeds
Playlists

Speech intelligence

Speech-to-text translation
Sentiment analysis
Call summarizations
Intent recognition
Tone and emotion detection
Keyword extraction
Chatbots

Marketing processes

Predictive segmentation
Content generation
Email campaigns
Automated social posting
Real-time ad bidding
A/B testing
Lead scoring
Personalized web experiences

Speech intelligence is the quiet winner
AI technologies like speech-to-text and audio intelligence power an incredible number of end-user products that, in turn, improve customer experiences. This could suggest that the demand for speech intelligence capabilities is even higher than reported.

Leaders are also clear on what they need to support these use cases—with Speech AI at the top of everyone’s list.

What Speech AI features have you incorporated or do you plan to incorporate into your products?

Audio intelligence

39%

43%

18%

Speech-to-text

55%

34%

12%

LLM capabilities

77%

16%

Currently using

Would like to incorporate

Not incorporating

The future is multimodal

Single-modal AI isn’t cutting it.

“Multimodal will be the biggest disruptor in our space.”

–Respondent, Head of Product

“There’s going to be an explosion of innovation, new apps, and new companies.”

–Respondent, Head of Product

Which AI modality do you think will be the most transformative for your industry?

57%Multimodal

22%Text

11%Video

9%Speech

1%Other

The release of viral tech like ChatGPT triggered a near-instant race to adopt AI and sent companies scrambling to integrate it into their products. While many of these players initially entered the space with a single-modal approach, it didn’t take long for businesses to understand that a hyper-focused, multimodal AI strategy is imperative to fully meet the vast set of customer needs.

Chapter 03

To build or not to build

That’s the (biggest) question.

Implementing AI may seem straightforward, but the wrong route can come with unforeseen risks, costs, and complications. We talked to other industry leaders to learn how they approached—and answered—the foundational question of build or buy.

Do a lot of research before deciding to go with your own AI because 90% of the time it’s the wrong decision. You have to have a huge amount of data to train a model to the standards that these other providers have.

–Respondent, Founder

It's like trying to race a rocket. You don't compete with machines.You either build a machine or buy a machine.

–Respondent, Founder

Unless you're an AI company, buy it off the shelf. Companies are deep in their journey. Why not leverage what they've learned, over trying to do it yourself.

–Respondent, Founder

One thing’s for sure

Integrating AI is harder than it seems.

What have been your biggest barriers to integrating AI into your products?

Learning curve

49%

Integrating with other tools

45%

Time to customize

41%

Staff bandwidth

36%

No barriers, it’s been easy

12%

Other

These barriers emphasize the importance of building a strong AI strategy prior to integration. Best practices such as considering user value, setting measurable goals, and creating action plans will help teams get to market faster.

In-house, open source, or AI provider

A decision worth taking your time with.

“They’re getting better, but do not underestimate how challenging it is to get open-source models to run predictably and produce high-quality results.”

–Respondent, Co-founder

68%

of respondents would rather partner with an AI provider than build their own solution

An open-source dilemma

The complexities you didn’t see coming.

“If you are using an open-source model, the learning curve is very steep. A closed API is the best top-tier quality in terms of outputs.”

–Respondent, Founder

The majority of respondents reported drawbacks to open-source building—most notably, the lack of support and subsequent strain on internal teams.

Do you see drawbacks in building on open-source models vs. buying an off-the-shelf solution?

What drawbacks do you encounter in building on open-source models?

67%

Less support and troubleshooting

63%

Internal bandwidth required to update

57%

Lack of capabilities

14%

Other

Build in-house or out?

Why leaders are reluctant to run solo.

“Unless you work with an experienced developer that can guide you through the tech, it can be a recipe for disaster.”

–Respondent, Head of Product

Why did you decide to use an AI partner instead of building your own?

Time to market

66%

Engineering capacity

58%

Cost

51%

Current models meet our needs

43%

Cutting-edge AI tech

40%

Other

The hidden costs of DIY AI

The financial burden of in-house implementation can add up fast. It often requires significant investments in hiring top-tier AI talent, plus the additional allocation of resources for infrastructure, ongoing model maintenance, updates, privacy compliance, and more.

Open source options, while convenient in many ways, can also come with drawbacks such as the need for extensive customization, the lack of dedicated support, and the burden of handling security risks and long-term scalability.

Partnering with an AI provider can lighten the load in more ways than one. Private companies provide ready-to-use solutions, ongoing support, and the latest advancements in AI models. Unlike in-house options which require constant retraining, providers keep customers on the cutting edge with direct access to the latest models. This allows businesses to focus on innovation without the overhead of AI maintenance and boasts a faster time-to-market, a more predictable cost structure, and greater scalability in the long run.

“We focus on delivering customer value early, so we very often decide to buy rather than build.”

–Respondent, Head of Product

“Developing a private AI model is necessary in some niche circumstances, but, for the most part, an AI provider can provide more advanced tech faster.”

–Respondent, Head of Product

What matters most

The top non-negotiables for teams evaluating vendors.

When it comes to the vendor research stage, there are some clear top-line factors respondents are evaluating.

What are the top 5 most important factors you look for in an AI vendor?

64%

Cost

58%

Quality and performance

47%

Accuracy

40%

Ease of use and configuration

37%

API and developer resources

From one founder to another

Advice from industry leaders on AI integration.

Be leading edge, but not bleeding edge. Embrace it, but start slowly. Test and scale.

–Respondent, GTM & Operations Leader

Use an AI provider for as long as possible. The technology is evolving quickly—you won't be able to keep pace with your own tech.

–Respondent, Founder

Don't try to incorporate just because it is the current buzzword. Have a realistic feel for what AI can do to make your product better and help your customer.

–Respondent, Founder

In summary

Current AI trends and our predictions for the year ahead.

The speed at which AI is moving is not just fast, it’s exponential. So, what’s different today? For starters, AI is no longer optional. While the tech’s initial swing applied mostly to internal automation, this report suggests a significant shift outward. AI has made its home in end-user products.

Despite a significant learning curve, industry leaders are no less eager to integrate. What we’re seeing as a primary workaround, is a strategic partnership with AI providers that deliver cutting-edge capabilities and handle the heavy lifting. As a result of these partnerships, businesses are thinking bigger than ever before, moving faster than ever before, and going all in on multimodal AI.

Staying competitive means staying agile, and agility in 2025 will be a whole new ball game. Based on these findings, we predict that companies building 360-degree product solutions with leading AI providers will be the ones not only creating the curve but staying ahead of it.

Methodology

Our quantitative insights were gathered from survey results of 200 participants. Additionally, we interviewed 11 professionals about their current AI experiences for qualitative insights.

Our respondents

By industry

48% Technology (software and hardware)13% AI development12% Business consulting9% Marketing6% Finance/FinTech5% Media2% Manufacturing2% Entertainment2% Automotive2% Logistics2% Gaming

By title

61% Founder or Co-founder20% Engineer or Developer12% Product Manager7% GTM & Operations

Time spent in a respective role

27% More than 10 years22% 6–10 years28% 3–5 years16% 1–2 years7% Less than 6 months to less than a year

By age

The average age was 43 years old

The code to meaningful
voice data

Partner with the leader in Speech AI to build powerful products with breakthrough industry impact.

Try our API for free Contact sales

import assemblyai as aai

transcriber = aai.Transcriber()
transcript = transcriber.transcribe(URL, config)

print(transcript)

{
  "id": "6rlr37h8f4-e310-4e23-bbf3-ea5f347dc684",
  "language_code": "en_us",
  "status": "completed",
  "text": "Runner's knee is a condition characterized by pain behind or around the kneecap...",
  "confidence": 0.98122,
  "audio_duration": 3200,
  "words": [
    { "text": "Runner's", "start": 0, "end": 550, "speaker": "A", "confidence": 0.98113 },
    { "text": "knee", "start": 580, "end": 1130, "speaker": "A", "confidence": 0.95417 }
  ]
}